Macroweather precipitation variability up to global and centennial scales
Bibliographic record
Abstract
Abstract We investigate precipitation variability in the “macroweather” regime—the intermediate regime between the familiar weather and climate regimes—which is associated to time scales from about 10 days to 30–100 years. Macroweather precipitation is characterized by negative fluctuation exponents. This implies—contrary to the weather regime—that fluctuations tend to cancel each other out, they diminish with time scale, this is important for seasonal, annual, and decadal forecasts. Aiming at a wide‐scale range space‐time statistical description of macroweather precipitation, we study the scaling of three centennial, global‐scale precipitation products (one gauge based, one reanalysis based, and one satellite based) and systematically compare them over wide ranges of time and space scales. Although these products have very similar temporal statistics, at 5° resolution, they only agree with each other after being averaged over scales of several years, at scales larger than 2–3 decades, they disagree again. In space, there is less agreement on the statistics but—since the data have low resolutions (mostly 5° × 5°)—the disagreement is only over a small overall range of scales: the monthly data agree fairly well at scales 20°–30° and larger. Moreover, we quantify the outer scale limit of the temporal scaling (20–40 years, depending on the product, on the spatial scale, pixel, or global). Overall, results show that precipitation can be modeled with space‐time scaling processes. The improved understanding of the space‐time macroweather precipitation variability and the limitations of precipitation products provided by this work opens new perspectives to the stochastic modeling and forecasting of macroweather precipitation as well as separating natural and anthropogenic precipitation.
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How this classification was reachedexpand
Full frame machine prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. The Gemma side is a direct model label for every work in the frame, read from the title-only record. The Codex side is a classifier learned from the 10,348 direct Codex labels and calibrated to design-weighted sample rates; fields without enough sample support carry no Codex call. Candidate is the union of the two sides; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels.
Distilled classifier scores by category (both heads)
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.000 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.001 | 0.000 |
Machine scores (provisional)
The two teacher heads of the student model, read on this work. A score orders the frame for review; it never asserts a category, and the validation status ships verbatim with every row.
Baseline scores from an immature model (maturity gate not passed, 7 training rounds). Scores rank; they never assert a category.
score_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from itClassification
machine, unvalidatedMachine predicted; a candidate call from one source (direct Gemma or distilled Codex), not a consensus.
How this classification was reached, model by model and score by score, is at the end of the page under "How this classification was reached".